package com.micro.ai.models.service.impl;

import com.micro.ai.models.entity.FineTuningJob;
import com.micro.ai.models.service.TaskSchedulerService;
import lombok.extern.slf4j.Slf4j;
import org.springframework.stereotype.Service;

/**
 * 基于消息队列的任务调度服务实现
 * 
 * 实现方案说明：
 * 1. 使用RabbitMQ/Redis/Kafka等消息队列
 * 2. 将训练任务消息发送到队列
 * 3. 由消费者（Worker）异步处理训练任务
 * 4. 定期轮询或通过回调更新任务进度
 * 
 * @author micro-ai
 * @since 0.0.1
 */
@Slf4j
@Service
public class MessageQueueTaskSchedulerServiceImpl implements TaskSchedulerService {

    // 方案1: 使用RabbitMQ（需要添加依赖）
    // @Autowired(required = false)
    // private RabbitTemplate rabbitTemplate;

    // 方案2: 使用Redis Streams（需要添加依赖）
    // @Autowired(required = false)
    // private StringRedisTemplate redisTemplate;

    // 方案3: 使用Kafka（需要添加依赖）
    // @Autowired(required = false)
    // private KafkaTemplate<String, String> kafkaTemplate;

    @Override
    public String submitTrainingJob(FineTuningJob job) {
        log.info("提交训练任务到消息队列: jobId={}, jobName={}", job.getId(), job.getJobName());
        
        // 方案1: RabbitMQ实现示例
        /*
        try {
            FineTuningJobMessage message = new FineTuningJobMessage();
            message.setJobId(job.getId());
            message.setBaseModelId(job.getBaseModelId());
            message.setDatasetId(job.getDatasetId());
            message.setTrainingParameters(job.getTrainingParameters());
            
            rabbitTemplate.convertAndSend("fine-tuning.exchange", "fine-tuning.routing-key", message);
            
            log.info("训练任务已发送到队列: jobId={}", job.getId());
            return job.getId(); // 或返回队列消息ID
            
        } catch (Exception e) {
            log.error("提交训练任务失败: jobId={}, error={}", job.getId(), e.getMessage(), e);
            throw new BusinessException("M0003", "提交训练任务失败: " + e.getMessage());
        }
        */
        
        // 方案2: Redis Streams实现示例
        /*
        try {
            FineTuningJobMessage message = new FineTuningJobMessage();
            message.setJobId(job.getId());
            message.setBaseModelId(job.getBaseModelId());
            message.setDatasetId(job.getDatasetId());
            message.setTrainingParameters(job.getTrainingParameters());
            
            redisTemplate.opsForStream().add("fine-tuning-stream", 
                StreamRecords.newRecord()
                    .ofObject(JSON.toJSONString(message))
                    .withStreamKey("fine-tuning"));
            
            log.info("训练任务已发送到Redis Stream: jobId={}", job.getId());
            return job.getId();
            
        } catch (Exception e) {
            log.error("提交训练任务失败: jobId={}, error={}", job.getId(), e.getMessage(), e);
            throw new BusinessException("M0003", "提交训练任务失败: " + e.getMessage());
        }
        */
        
        // 方案3: Kafka实现示例
        /*
        try {
            FineTuningJobMessage message = new FineTuningJobMessage();
            message.setJobId(job.getId());
            message.setBaseModelId(job.getBaseModelId());
            message.setDatasetId(job.getDatasetId());
            message.setTrainingParameters(job.getTrainingParameters());
            
            kafkaTemplate.send("fine-tuning-topic", job.getId(), JSON.toJSONString(message));
            
            log.info("训练任务已发送到Kafka: jobId={}", job.getId());
            return job.getId();
            
        } catch (Exception e) {
            log.error("提交训练任务失败: jobId={}, error={}", job.getId(), e.getMessage(), e);
            throw new BusinessException("M0003", "提交训练任务失败: " + e.getMessage());
        }
        */
        
        // 当前为示例实现，实际使用时需要：
        // 1. 添加消息队列依赖（RabbitMQ/Redis/Kafka）
        // 2. 配置连接信息
        // 3. 实现消费者（Worker）处理训练逻辑
        // 4. 实现进度更新机制
        
        log.warn("消息队列未配置，跳过实际任务提交: jobId={}", job.getId());
        return job.getId();
    }

    @Override
    public void cancelTrainingJob(String jobId, String externalJobId) {
        log.info("取消训练任务: jobId={}, externalJobId={}", jobId, externalJobId);
        
        // 方案1: 发送取消消息到队列
        /*
        try {
            CancelJobMessage message = new CancelJobMessage();
            message.setJobId(jobId);
            message.setExternalJobId(externalJobId);
            
            rabbitTemplate.convertAndSend("fine-tuning.exchange", "cancel.routing-key", message);
            
        } catch (Exception e) {
            log.error("取消训练任务失败: jobId={}, error={}", jobId, e.getMessage(), e);
            throw new BusinessException("M0003", "取消任务失败: " + e.getMessage());
        }
        */
        
        log.warn("消息队列未配置，跳过实际任务取消: jobId={}", jobId);
    }

    @Override
    public TaskStatus queryTaskStatus(String externalJobId) {
        log.info("查询任务状态: externalJobId={}", externalJobId);
        
        // 方案1: 从Redis缓存中查询
        /*
        try {
            String statusJson = redisTemplate.opsForValue().get("job-status:" + externalJobId);
            if (statusJson != null) {
                return JSON.parseObject(statusJson, TaskStatus.class);
            }
        } catch (Exception e) {
            log.error("查询任务状态失败: externalJobId={}, error={}", externalJobId, e.getMessage(), e);
        }
        */
        
        // 如果使用外部平台（如OpenAI），需要调用其API查询状态
        // 如果使用Kubernetes，需要查询Job状态
        
        return null;
    }

    /**
     * 训练任务消息（示例）
     */
    public static class FineTuningJobMessage {
        private String jobId;
        private String baseModelId;
        private String datasetId;
        private String trainingParameters;

        // Getters and Setters
        public String getJobId() {
            return jobId;
        }

        public void setJobId(String jobId) {
            this.jobId = jobId;
        }

        public String getBaseModelId() {
            return baseModelId;
        }

        public void setBaseModelId(String baseModelId) {
            this.baseModelId = baseModelId;
        }

        public String getDatasetId() {
            return datasetId;
        }

        public void setDatasetId(String datasetId) {
            this.datasetId = datasetId;
        }

        public String getTrainingParameters() {
            return trainingParameters;
        }

        public void setTrainingParameters(String trainingParameters) {
            this.trainingParameters = trainingParameters;
        }
    }
}

